CN115602328A - Early warning method and device for acute leukemia - Google Patents
Early warning method and device for acute leukemia Download PDFInfo
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Abstract
The embodiment of the invention discloses an early warning method and device for acute leukemia, which comprises the steps of collecting current detection data and user information of a user, inputting the current detection data into a pre-established prediction model and outputting a prediction result; when the prediction result is larger than the first threshold value, acquiring a verification process and sending the verification process to the user terminal; and when the prediction result is between the first threshold and the second threshold, inputting the current detection data into the early warning model to obtain the probability of the disease risk. The method comprises the steps of collecting detection data of a user who has undergone preliminary detection, inputting corresponding prediction results by combining a prediction model trained in advance, carrying out subsequent processes in different modes according to different prediction results, further carrying out confirmed diagnosis verification, prompting the user verification process according to user information collected in advance, or carrying out early warning of illness, informing of illness risk probability, and reducing the detection cost of a user who is predicted to be not ill through scientific judgment.
Description
Technical Field
The invention relates to the technical field of medical diagnosis, in particular to an early warning method and device for acute leukemia.
Background
Acute myelocytic leukemia (also called acute myelocytic leukemia or acute myelocytic leukemia) is a hematological malignancy characterized by abnormal proliferation and differentiation of bone marrow cells, and is also the main pathological type (accounting for about 80-90%) of acute leukemia of adults, and has complex clinical manifestation, acute and serious illness in most cases and serious risk in advance, and the life can be threatened if the acute myelocytic leukemia is not treated in time. Conventionally, as a diagnosis of the initial onset of leukemia, there is a method in which the number of leukocytes in peripheral blood of a patient is measured, and when the measured value exceeds a normal value, the occurrence of leukemia is suspected. However, even in diseases other than leukemia such as cold, the number of leukocytes increases due to an increase in the immune response in vivo, and therefore, measurement based on the number of leukocytes alone may result in false positives. In addition, the normal value of the number of leukocytes in peripheral blood is 4,000 to 8,000/μ L, and the range is wide, and false negatives may occur. The further diagnosis of acute myelocytic leukemia mainly depends on peripheral blood cell examination and bone marrow biopsy, the detection cost is high, and early scientific early warning aiming at the examination data is lacked.
Disclosure of Invention
Aiming at the defects, the embodiment of the invention discloses an acute leukemia early warning method and device, which are low in prediction cost and capable of giving early warning analysis for detecting diseases to a user.
The embodiment of the invention discloses a method for early warning acute leukemia in a first aspect, which comprises the following steps:
acquiring current detection data and user information of a user, inputting the current detection data into a pre-established prediction model, and outputting a prediction result, wherein the prediction result is a data score;
when the prediction result is larger than a first threshold value, acquiring a verification process, and sending the verification process to the user terminal according to the user information;
and when the prediction result is between a first threshold and a second threshold, inputting the current detection data into an early warning model to obtain the risk probability of the disease, wherein the first threshold is larger than the second threshold.
As an alternative implementation, in the first aspect of the embodiment of the present invention, the prediction model is built by:
acquiring a plurality of groups of first historical detection data, wherein the first historical detection data comprises inspection items, detection data and diagnosis results;
and performing model training by using the detection data and the inspection items as model input data and using the diagnosis result as model output data to obtain a prediction model.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the establishing the prediction model further includes:
and acquiring a plurality of groups of second historical detection data, and correcting the prediction model according to the second historical detection data.
As an alternative implementation manner, in the first aspect of the embodiments of the present invention, the disease verification process includes a blood routine test, a peripheral blood smear item, and a bone marrow biopsy item.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, when the prediction result is greater than the first threshold, the expert registration prompt message is further sent to the user terminal, and after receiving a confirmation response from the user terminal, an expert outpatient service appointment instruction is automatically sent.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the early warning model is established in the following manner:
constructing an early warning sample database, wherein the early warning sample database comprises historical detection data with data scores between a first threshold and a second threshold, and review data of a user within a preset time corresponding to each group of historical detection data;
and training an early warning model according to the historical detection data and the review data between the first threshold and the second threshold of the data scores.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, training the early warning model according to the historical detection data and the review data with the data score between the first threshold and the second threshold includes:
setting a plurality of value ranges corresponding to each item of inspection data in the historical inspection data with each group of data score between a first threshold and a second threshold, and dividing the historical inspection data with the values of the inspection data belonging to the same value range into the same type of historical inspection data;
calculating the disease risk probability corresponding to each group of historical detection data in the same type of historical detection data according to the re-diagnosis data of each group of historical detection data;
and taking the historical detection data with the data score between the first threshold and the second threshold as input data, and taking the risk of illness probability corresponding to each type of historical detection data as output data to train an early warning model.
The second aspect of the embodiment of the invention discloses an acute leukemia early warning device, which comprises:
a data acquisition module: the system comprises a data acquisition module, a prediction module and a data processing module, wherein the data acquisition module is used for acquiring current detection data and user information of a user, inputting the current detection data into a pre-established prediction model and outputting a prediction result, and the prediction result is a data score;
the verification flow sending module: the verification flow is obtained when the prediction result is larger than a first threshold value, and the verification flow is sent to the user terminal according to the user information;
sick risk early warning module: and when the prediction result is between a first threshold and a second threshold, inputting the current detection data into an early warning model to obtain the risk probability of the disease, wherein the first threshold is greater than the second threshold.
As an alternative implementation, in the second aspect of the embodiment of the present invention, the prediction model is built by:
acquiring a plurality of groups of first historical detection data, wherein the first historical detection data comprises inspection items, detection data and diagnosis results;
and performing model training by using the detection data and the inspection items as model input data and using the diagnosis result as model output data to obtain a prediction model.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the establishing the prediction model further includes:
and acquiring a plurality of groups of second historical detection data, and correcting the prediction model according to the second historical detection data.
As an alternative implementation, in the second aspect of the embodiment of the present invention, the disease verification process includes a blood routine test, a peripheral blood smear item, and a bone marrow biopsy item.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, when the prediction result is greater than the first threshold, the expert registration prompt message is further sent to the user terminal, and after receiving a confirmation response from the user terminal, the expert outpatient service appointment instruction is automatically sent.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the early warning model is established by:
constructing an early warning sample database, wherein the early warning sample database comprises historical detection data with data scores between a first threshold and a second threshold, and review data of a user corresponding to each group of the historical detection data within a preset time;
and training an early warning model according to the historical detection data and the review data between the first threshold and the second threshold of the data scores.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, training the early warning model according to the historical detection data and the review data with the data score between the first threshold and the second threshold includes:
setting a plurality of value ranges corresponding to each item of inspection data in the historical inspection data with each group of data score between a first threshold and a second threshold, and dividing the historical inspection data with the values of the inspection data belonging to the same value range into the same type of historical inspection data;
calculating the disease risk probability corresponding to each group of historical detection data in the same type of historical detection data according to the re-diagnosis data of each group of historical detection data;
and taking the historical detection data with the data score between the first threshold and the second threshold as input data, and taking the disease risk probability corresponding to each type of historical detection data as output data to train an early warning model.
A third aspect of an embodiment of the present invention discloses an electronic device, including: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory for executing the acute leukemia early warning method disclosed by the first aspect of the embodiment of the invention.
A fourth aspect of the embodiments of the present invention discloses a computer-readable storage medium storing a computer program, where the computer program enables a computer to execute the method for warning acute leukemia disclosed in the first aspect of the embodiments of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the early warning method for the acute leukemia, disclosed by the embodiment of the invention, the detection data of the user who has undergone preliminary detection is collected, the corresponding prediction result is input by combining a prediction model trained in advance, the subsequent process is carried out in different modes according to the prediction result, the user with the prediction result larger than the first threshold value is further subjected to confirmation check, the user check process is prompted according to the user information collected in advance, the user with the prediction result between the first threshold value and the second threshold value is subjected to disease early warning, the disease risk probability is informed, the detection cost of the user is reduced through scientific judgment, and the disease risk is prompted to help to increase the health care consciousness of the user and help the user to make disease response measures in advance.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of an acute leukemia warning method disclosed in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a construction process of a prediction model disclosed in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a construction process of an early warning model disclosed in the embodiment of the present invention;
fig. 4 is a schematic structural diagram of an acute leukemia early warning device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first", "second", "third", "fourth", and the like in the description and the claims of the present invention are used for distinguishing different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses an acute leukemia early warning method, an acute leukemia early warning device, electronic equipment and a storage medium, wherein detection data of a user who has undergone preliminary detection are collected, a corresponding prediction result is input by combining a prediction model which is trained in advance, follow-up processes are carried out in different modes according to the prediction result, the user whose prediction result is larger than a first threshold value is further subjected to confirmed diagnosis verification, the user verification process is prompted according to user information which is collected in advance, a disease early warning is carried out on the user whose prediction result is between the first threshold value and a second threshold value, the disease risk probability is notified, the detection cost of the user is reduced through scientific judgment, the disease risk is prompted to the user, the health care consciousness of the user is increased, and the user is helped to make disease countermeasures in advance.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of an acute leukemia warning method according to an embodiment of the present invention. The execution main body of the method described in the embodiment of the present invention is an execution main body composed of software or/and hardware, and the execution main body can receive related information in a wired or/and wireless manner and can send a certain instruction. Of course, it may also have certain processing and storage functions. The execution body may control a plurality of devices, such as a remote physical server or a cloud server and related software, or may be a local host or a server and related software for performing related operations on a device installed somewhere. In some scenarios, multiple storage devices may also be controlled, which may be co-located with the device or located in a different location. As shown in fig. 1, the acute leukemia-based early warning method comprises the following steps:
s101, collecting current detection data and user information of a user, inputting the current detection data into a pre-established prediction model, and outputting a prediction result, wherein the prediction result is a data score.
In the embodiment, the user refers to a user who goes to a hospital for registration and performs corresponding leukemia preliminary screening. The initial purpose of the preliminary screening program for leukemia is not necessarily to examine whether leukemia is detected or not, but may be to perform other physical examinations, and the examination is also required. The value of the item can be used for preliminarily verifying whether leukemia is suffered. At this time, the user may or may not be a leukemia patient through a leukemia preliminary screening. In the application, whether the user has diagnosed the leukemia or not is not directly diagnosed, but the possibility of incapability is presumed through different numerical value ranges in the current detection data, so that further early warning measures are taken. The user information generally includes a user name used for binding with the user and confirming identity information, and a user contact way used for contacting with the user so as to send related information to the user according to the prediction result. The user contact information can comprise a mobile phone number, a mail and the like, information such as a prediction result and the like can be sent to the user subsequently through the user contact information, and the user information can be collected through a corresponding application program in advance or collected when the user registers. The user information may include the user's age, sex, etc., in addition to the user name and the user contact information.
The prediction model of the embodiment is obtained by training in advance through a large amount of detection data and carrying out corresponding inspection and correction. The prediction model is used for helping to predict whether the corresponding user has a disease risk based on various index values of current detection data, can predict in advance, and combines a scientific automatic prediction method on the basis of traditional artificial judgment, so that the detection cost of non-patients is reduced, and the overall examination time is shortened.
Specifically, referring to fig. 2, the prediction model is constructed by: 1011. a plurality of groups of first historical detection data are obtained, and the first historical detection data comprise inspection items, detection data and diagnosis results. 1012. And carrying out model training according to the detection data, the inspection items and the diagnosis result to obtain a prediction model. The first historical test data of the embodiment is screening data obtained from previous leukemia preliminary screening items performed by different users, and corresponds to different examination items, test data and diagnosis results, such as blood routine tests, which include different examination items, such as white blood cell count, red blood cell count, etc. The data result according to the blood routine is usually pre-judged corresponding to the detection result, that is, data scores for different detection data are generated, when the data score is greater than a first threshold, it indicates that the possibility of the disease is high, when the data score is between the first threshold and a second threshold, it indicates that the probability of the disease is small but there is a certain probability, further early warning analysis is needed, and when the data score is lower than the second threshold, it indicates that there is no risk, and the pre-judgment is the diagnosis result in this step.
Further, the embodiment of establishing the prediction model further comprises: 1013. and acquiring a plurality of groups of second historical detection data 1014, and correcting the prediction model according to the second historical detection data. In this step, the second historical inspection data is an inspection including the same inspection item as the first historical inspection data. That is, a sufficiently large amount of historical detection data is obtained, and the historical detection data is divided into two parts according to a certain proportion, wherein one part is used as first historical detection data, and the other part is used as second historical detection data. The ratio may be the first historical detected data in six, the second historical detected data in four, or other ratios. By dividing the historical detection data into two parts, one part is used for constructing a prediction model, and the other part is used for correcting the model, the model is more accurate, and the prediction result is more credible.
And S102, when the prediction result is larger than the first threshold value, acquiring a verification process, and sending the verification process to the user terminal according to the user information.
Different data scores can be obtained through the prediction model, and the data scores correspond to different threshold ranges according to the heights of the data scores. When the prediction result is larger than the first threshold value, in order to facilitate subsequent confirmation, recheck and the like of the patient, the relevant verification process is called and sent to the user according to the user information, so that the user can know the subsequent items to be checked in advance according to the verification process. Meanwhile, the verification process can correspondingly comprise the place, time, price and the like corresponding to each verification item, and convenience is provided. It should be noted that this step is only a routine procedure for providing the user with the existing leukemia diagnosis, and not how to diagnose whether the user has leukemia.
Further, the embodiment also sends an expert registration prompt message to the user terminal, and automatically sends an expert outpatient service appointment instruction after receiving a confirmation response from the user terminal. For example, the background calls all corresponding expert information including expert names, expert sitting time, expert vacancy visiting name amounts and the like, sends an expert registration prompt message to a user terminal, the user terminal generally corresponds to a smart phone of a user, the expert registration prompt message comprises the expert information and prompt registration information, the prompt registration information prompts the user to register, a response button can be generated to send the response button to the user, the button comprises confirmation registration and neglect, when a response instruction of the user for confirming registration is received, the expert information is displayed so that the user can select, a proper expert is selected, and the system background automatically makes an appointment for registration after selection. As another example, the background can also collect the time of the instant noodle when receiving a response instruction of the user for confirming registration, and automatically select an expert for appointment registration based on the time. The preference can include ranking of experts with algorithm weight calculation, wherein the experts with proper time are selected firstly, the experts with high level are selected secondly, and the experts with the same level and proper time can be selected to see more people.
S103, when the prediction result is between a first threshold and a second threshold, inputting the current detection data into an early warning model to obtain the risk probability of the disease, wherein the first threshold is larger than the second threshold.
In this step, with reference to fig. 3, the early warning model is established in the following manner: 1031. constructing an early warning sample database, wherein the early warning sample database comprises historical detection data with data scores between a first threshold and a second threshold, and review data of a user corresponding to each group of historical detection data within a preset time; and training an early warning model according to the historical detection data and the review data between the first threshold and the second threshold of the data score. The users with the data scores between the first threshold value and the second threshold value, namely the users with less possibility of diseases are predicted by the application through the prediction model in the first step, but because the numerical values are not absolutely healthy, the users may have the possibility of diseases in the future, further early warning monitoring is carried out, the possibility of diseases in the future corresponding to the users is calculated through the early warning model, and the users can be reminded of regular examination, timely review and the like.
Specifically, training the early warning model according to the historical detection data and the review data between the first threshold and the second threshold of the data score includes:
1032. setting a plurality of numerical value ranges for each item of inspection data in the historical inspection data between the first threshold and the second threshold corresponding to each group of data scores, and dividing the historical inspection data of which the numerical values belong to the same numerical value range into the same type of historical inspection data;
1033. calculating the disease risk probability corresponding to each group of historical detection data in the same type of historical detection data according to the re-diagnosis data of each group of historical detection data;
1034. and taking the historical detection data with the data score between the first threshold and the second threshold as input data, and taking the disease risk probability corresponding to each type of historical detection data as output data to train an early warning model.
Example two
Referring to fig. 4, fig. 4 is a schematic structural diagram of an acute leukemia warning device according to an embodiment of the present invention. As shown in fig. 4, the acute leukemia early warning device may include a data acquisition module 401, a verification process sending module 402, and a disease risk early warning module 403, where the data acquisition module 401 is configured to acquire current detection data of a user and user information, input the current detection data into a pre-established prediction model, and output a prediction result, where the prediction result is a data score; an verification process sending module 402, configured to obtain a verification process when the prediction result is greater than the first threshold, and send the verification process to the user terminal according to the user information; and an illness risk early warning module 403, configured to, when the prediction result is between a first threshold and a second threshold, input the current detection data to an early warning model to obtain an illness risk probability, where the first threshold is greater than the second threshold.
In the above, the prediction model is established by the following method: acquiring a plurality of groups of first historical detection data, wherein the first historical detection data comprises inspection items, detection data and diagnosis results; and carrying out model training according to the detection data, the inspection items and the diagnosis result to obtain a prediction model. Further, establishing the prediction model further includes: and acquiring a plurality of groups of second historical detection data, and correcting the prediction model according to the second historical detection data.
The diseased examination procedure described in the examination procedure transmitting module 402 includes a blood routine examination, a peripheral blood smear item, and a bone marrow biopsy item. The verification flow sending module 402 also sends an expert registration prompt message to the user terminal, and automatically sends an expert outpatient appointment instruction after receiving a confirmation response from the user terminal.
In the disease risk early warning module 403, the early warning model is established in the following manner: constructing an early warning sample database, wherein the early warning sample database comprises historical detection data with data scores between a first threshold and a second threshold, and review data of a user within a preset time corresponding to each group of historical detection data; and training an early warning model according to the historical detection data and the review data between the first threshold and the second threshold of the data score.
Further, training an early warning model according to the historical detection data and the review data with the data score between the first threshold and the second threshold comprises: setting a plurality of numerical value ranges for each item of inspection data in the historical inspection data between the first threshold and the second threshold corresponding to each group of data scores, and dividing the historical inspection data of which the numerical values belong to the same numerical value range into the same type of historical inspection data; calculating the disease risk probability corresponding to each group of historical detection data in the same type of historical detection data according to the re-diagnosis data of each group of historical detection data; and taking the historical detection data with the data score between the first threshold and the second threshold as input data, and taking the risk of illness probability corresponding to each type of historical detection data as output data to train an early warning model.
EXAMPLE III
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. The electronic device may be a computer, a server, or the like, and certainly, may also be an intelligent device such as a mobile phone, a tablet computer, a monitoring terminal, or the like, and an image acquisition device having a processing function. As shown in fig. 5, the electronic device may include:
a memory 501 in which executable program code is stored;
a processor 502 coupled to the memory 501;
the processor 502 calls the executable program code stored in the memory 501 to execute some or all of the steps in the method for warning acute leukemia according to the first embodiment.
The embodiment of the invention discloses a computer-readable storage medium which stores a computer program, wherein the computer program enables a computer to execute part or all of the steps in the acute leukemia early warning method in the first embodiment.
The embodiment of the invention also discloses a computer program product, wherein when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the acute leukemia early warning method in the first embodiment.
The embodiment of the invention also discloses an application publishing platform, wherein the application publishing platform is used for publishing a computer program product, and when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the acute leukemia early warning method in the first embodiment.
In various embodiments of the present invention, it should be understood that the sequence numbers of the processes do not imply a necessary order of execution, and the order of execution of the processes should be determined by functions and internal logics of the processes, and should not limit the implementation processes of the embodiments of the present invention in any way.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present invention, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the method according to the embodiments of the present invention.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood, however, that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
Those of ordinary skill in the art will appreciate that some or all of the steps in the methods of the embodiments described herein may be implemented by hardware associated with a program that may be stored in a computer-readable storage medium, including a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable Programmable Read-Only Memory (EEPROM), an optical Disc-Read-Only Memory (CD-ROM) or other storage medium capable of storing data, a magnetic tape, or any other computer-readable medium capable of carrying a computer program or computer-readable data.
The method, the device, the electronic device and the storage medium for acute leukemia early warning disclosed by the embodiment of the invention are described in detail, a specific example is applied in the description to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An early warning method for acute leukemia, which is characterized by comprising the following steps:
acquiring current detection data and user information of a user, inputting the current detection data into a pre-established prediction model, and outputting a prediction result, wherein the prediction result is a data score;
when the prediction result is larger than a first threshold value, acquiring a verification process, and sending the verification process to the user terminal according to the user information;
and when the prediction result is between a first threshold and a second threshold, inputting the current detection data into an early warning model to obtain the risk probability of the disease, wherein the first threshold is larger than the second threshold.
2. The warning method as claimed in claim 1, wherein the predictive model is built by:
acquiring a plurality of groups of first historical detection data, wherein the first historical detection data comprises inspection items, detection data and diagnosis results;
and carrying out model training according to the detection data, the inspection items and the diagnosis result to obtain a prediction model.
3. The early warning method of claim 2, wherein building the predictive model further comprises:
and acquiring a plurality of groups of second historical detection data, and correcting the prediction model according to the second historical detection data.
4. The warning method as claimed in claim 1, wherein the verification procedure includes a blood routine test, a peripheral blood smear item, and a bone marrow biopsy item.
5. The early warning method as claimed in claim 4, wherein when the prediction result is greater than the first threshold, an expert registration prompt message is further sent to the user terminal, and when a confirmation response is received from the user terminal, an expert outpatient service appointment instruction is automatically sent.
6. The warning method of claim 1, wherein the warning model is established by:
constructing an early warning sample database, wherein the early warning sample database comprises historical detection data with data scores between a first threshold and a second threshold, and review data of a user within a preset time corresponding to each group of historical detection data;
and training an early warning model according to the historical detection data and the review data between the first threshold and the second threshold of the data score.
7. The warning method of claim 6, wherein training a warning model based on historical detection data between a first threshold and a second threshold and review data for the data score comprises:
setting a plurality of value ranges corresponding to each item of inspection data in the historical inspection data with each group of data score between a first threshold and a second threshold, and dividing the historical inspection data with the values of the inspection data belonging to the same value range into the same type of historical inspection data;
calculating the disease risk probability corresponding to each group of historical detection data in the same type of historical detection data according to the repeated diagnosis data of each group of historical detection data;
and taking the historical detection data with the data score between the first threshold and the second threshold as input data, and taking the disease risk probability corresponding to each type of historical detection data as output data to train an early warning model.
8. An early warning device for acute leukemia, comprising:
a data acquisition module: the system comprises a data acquisition module, a prediction module and a data processing module, wherein the data acquisition module is used for acquiring current detection data and user information of a user, inputting the current detection data into a pre-established prediction model and outputting a prediction result, and the prediction result is a data score;
the verification flow sending module: the verification flow is obtained when the prediction result is larger than a first threshold value, and the verification flow is sent to the user terminal according to the user information;
sick risk early warning module: and the early warning module is used for inputting the current detection data into an early warning model to acquire the probability of the risk of illness when the prediction result is between a first threshold and a second threshold, wherein the first threshold is greater than the second threshold.
9. An electronic device, comprising: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory for performing the acute leukemia warning method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the method for warning of acute leukemia according to any one of claims 1 to 7.
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